Non-deterministic data generation is an avid pursuit in science dating back one and a half centuries. In some scientific circles truly random data is considered to be a representation of the essence of life and matter itself. Clandestine techniques of capturing non-deterministic data although slow, are currently in practice in college courses ranging from the study of statistics to physics to the ebb and flow of tides and the mutation of genes throughout human evolution. As humankind further adapts the modern computer to aid in scientific study, the appetite for randomness increases proportionally.
When random numbers are pulled from truly non-deterministic data, they can be used in a wide range of business applications ranging from fair lotteries, stochastic studies in finance, poker machines and security applications for business.
In his famous quote on the subject of randomness, John von Neumann clearly states “Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin. For, as has been pointed out several times, there is no such thing as a random number—there are only methods to produce random numbers, and a strict arithmetic procedure of course is not such a method.” John von Neumann, “Various techniques used in connection with random digits,” in A. S. Householder, G. E. Forsythe, and H. H. Germond, eds., Monte Carlo Method, National Bureau of Standards Applied Mathematics Series, 12 (Washington, D.C.: U.S. Government Printing Office, 1951): 36-38.
Computers are purposely designed to be stateful machines. An average desktop computer today can execute 100 million instructions per second. Computer programs are fundamentally based on mathematical calculations. Producing truly random data from computer algorithms, no matter how tricky or seemingly complex the algorithm may be, is not possible. Computer programs are able to produce data that appears statistically random in every way and for some applications this pseudo-random data will suffice. Other applications require data to be truly random. Truly random data is distilled from truly random physical events. This distillation process need not be solely based upon “whitening” or software compensation for biased distribution. If captured from more than one type of physical source and in a plurality of each type of source, the entropy can be allowed to choose its own path in terms of random distribution. The strength of a random stream of bits of this nature is derived from the diversity of the origin of its seeds and the freedom of the seeds to interact with non-deterministic, non-periodic timing throughout the sampling process.